Upload existing events via CSV

The CSV upload is a beta feature according to the Algolia Terms of Service (“Beta Services”).

Capturing events through the Insights API is important to continuously train and improve the Recommend models. Collecting enough events can take some time. You can import past user events from a CSV file to benefit from Recommend earlier.

Format of the CSV file with historical events

The CSV file with historical events must have the following format:

• The CSV file must be smaller than 100 MB.
• The first row must contain the strings userToken, timestamp, objectID, eventType, and eventName. Extra columns are ignored.
• Each row represents an event tied to a single objectID.
• The timestamps should cover a period of at least 30 days.

• Events older than 90 days are ignored.

  Some programs like Microsoft Excel add these characters when exporting a CSV file. They might not be visible in regular text editors. The easiest way to find and remove these characters is by using a code editor such as Visual Studio Code.

Each event must have the following properties:

For more information, see Event properties.

Upload historical events for Recommend

To import historical events from a CSV file, follow these steps:

1. Go to the Algolia dashboard and select your Algolia application.

2. On the left sidebar, select Recommend.

3. Select a model you want to train and click Start using.
4. In the Select data source section, select an Algolia index as a source for your recommendations.

5. In the Collect events section, click Upload .csv to upload your CSV file with the historical events.

   

Once you collected enough click and conversion events, Algolia Recommend only uses those events for training the model and discards the events from the CSV file.

You can also re-upload a CSV file. The training only takes the newer file into account and discards the old events.

Export events from Google Analytics 360 with BigQuery

If you track user events with Google Analytics, you can export these events with BigQuery. You can then save these events in a CSV file which you can import in the Algolia dashboard to start training your Recommend models.

Before you begin

Before you can export your events from Google Analytics with BigQuery, you must have:

• A Google Analytics 360 account with a website tracking ID.
• Enhanced Ecommerce activated and set up for your website.
• BigQuery Export enabled in Google Analytics 360 for setting up daily imports into BigQuery.

The productSKU from Google Analytics 360 must match the objectID in your index.

Set up a BigQuery export

You can adapt the following query to export the user events required to train Algolia Recommend models.

Replace the following variables:

• GCP_PROJECT_ID: the name of the project that holds the Analytics 360 data in BigQuery
• BQ_DATASET: the name of the dataset that stores the exported events
• DATE_FROM and DATE_TO with the corresponding dates in YYYY-MM-DD format for a time window of at least 30 days.

You can run this query in the SQL workplace for BigQuery or use one of the BigQuery API client libraries.

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WITH ecommerce_data AS (
    SELECT
        fullVisitorId as user_token,
        TIMESTAMP_SECONDS(visitStartTime + CAST(hits.time/1000 AS INT64)) as timestamp,
        products.productSKU as object_ids,
        CASE WHEN hits.eCommerceAction.action_type = "2" THEN 'click'
            WHEN hits.eCommerceAction.action_type = "3" THEN 'click'
            WHEN hits.eCommerceAction.action_type = "5" THEN 'click'
            WHEN hits.eCommerceAction.action_type = "6" THEN 'conversion'
        END
        AS event_type,
        CASE WHEN hits.eCommerceAction.action_type = "2" THEN "product_view"
            WHEN hits.eCommerceAction.action_type = "3" THEN "add_to_cart"
            WHEN hits.eCommerceAction.action_type = "5" THEN "checkout"
            WHEN hits.eCommerceAction.action_type = "6" THEN "purchase"
        END
        AS event_name
    FROM
        `GCP_PROJECT_ID.BQ_DATASET.ga_sessions_*`,
        UNNEST(hits) as hits,
        UNNEST(hits.product) as products
    WHERE
        _TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d',DATE('DATE_FROM')) AND FORMAT_DATE('%Y%m%d',DATE('DATE_TO'))
    AND
        fullVisitorId IS NOT NULL
    AND hits.eCommerceAction.action_type in UNNEST(["2", "3", "5", "6"])
),
dedup_ecommerce_data AS (
    SELECT user_token as userToken,
    timestamp, event_name as eventName,
    event_type as eventType,
    object_id as objectID
    FROM ecommerce_data
    GROUP BY userToken, timestamp, eventName, eventType, objectID
)

select * from dedup_ecommerce_data